Then, aggregation methods can also be applied to summarization to combine multiple ranking results into an aggregation ranking to generate the combined results.

table 1 shows the comparison results (F-measure) on DUC2004 data set in ROUGE-1, ROUGE-2 and ROUGE-SU4 along with corresponding 95% confidence intervals and Figure 2 gives an illustration of the comparison on ROUGE-1 metric (LexPR is short for LexPageRank shown in the figure).


In the final step 5 we use the trained SVM to re-rank the index output (Alg.

in our implementation of BOT, we assume that the sequential input order is taken into account.


While the index search often provides a reliable candidate, implicit features such as coherence, conceptmention probability and topic similarity are only partially graspable by Wiki and may induce a SVM re-ranking.

the nil prediction c 0 for Taiwan is not counted as a false positive, since we already observe History of China as a false positive, with two true positives from People's Rep. of China and Jiangsu resulting in P = 0.66.


(2012) study the notion of language coordination -a metric that measures the extent to which a discourse participant adopts another's language -in relation with various social attributes such as power, gender, etc.

for this purpose, we utilize the gold organizational hierarchy for Enron released by (Agarwal et al., 2012).


Any message that is not an upward message is labeled non-upward.

this roughly captures the "restriction of an interactant's action-environment", suggested as one of the key elements to identify exercise of power in interactions by Wartenberg (1990).


It contains relations between 1,518 employees, and 13,724 dominance pairs (pairs of employees such that the first dominates the second in the hierarchy, not necessarily immediately).

these 50 recipients will be added as data points in our κ calculation, thereby increasing the expected agreement and decreasing the κ value.


The table shows that: The AP 3 algorithm (DM_AP) achieves the best performance with the improvements of ~6 units in P and ~5 in MRR compared with DM.

this means when we consider dependency mappings between English and Chinese, we can safely ignore other relationships whose percentages are less than 2%.


It is worth noting that one dependency relationship in one language may not always be directly mapped to the same relationship in another language and there are even cases where one relationship may map to multiple relationships in another language (cf.

using linguistic knowledge from both English and Chinese languages, we manually craft 10 dependency mappings M Ψ between these two languages, as shown in Fig.


This suggests that weight optimization via perceptron algorithms substantially helps BLC.

in order to determine the optimal subsets in automatic mappings A Ψ and manual mappings M Ψ respectively, we conduct ablation tests on the development lexicon LN d (cf.


Here, as our goal is to build a lexicon of nouns occurring frequently in the source text, one reasonable assumption is that their translation counterparts also occur frequently in the target text.

we argue that this may not be optimal since different mappings may have different contributions to BLC.


Our result is thus in line with the precision performance of Smajda's XTRACT (Smadja, 1993) 19 .

the former is an effort for the creation of a database of Italian MWEs annotated according to their morpho-syntactic pattern, where MWEs were given from preexisting dictionaries.


Currently Log Likelihood and Pointwise Mutual Information have been implemented.

the AverageFrequency PreFilter filters away all collocations with f below the mean X .


Each DPMM expert is characterized by a concentration parameter, and we vary this parameter to induce different experts (clusterings).

we now face the task of aggregating the individual clusterings induced by the different DP experts.


Our goal of finding the target result in an end to end system is difficult to evaluate without deployment.

the Maximum Entropy classifier is trained on the annotated examples.


Although this information can be (and has been) leveraged for guiding users through a faceted search experience, we focus in this paper on the extraction of salient questions from unstructured data.


In studies of grammatical error detection and correction in ESL writing, spelling correction has been regarded as a preprocessing step in a pipeline.

", the result is as follows:    The experimental results of POS tagging is shown in Table 5.


In this graph, we distinguish two types of nodes: nests and plain nodes.

• The initial amount of energy per node (E 0 ) and the ant life-span (ω) influence the number of ants that can be produced and therefore the probability of reinforcing less likely paths.


The problem can become intractable even on the span of short sentences: a linguistically motivated context, such as a paragraph for instance, can not be handled.

the definition of a sense j of word i is noted d(w i, j ).


Contrarily to Banerjee, however, we do not consider the sum of squared sub-string overlaps, but merely a bag-of-words overlap that allows us to generate a dictionary from WordNet, where each word contained in any of the word sense definitions is indexed by a unique integer and where each resulting definition is sorted.

when an ant arrives on a nest of another term (that corresponds to a sense thereof), it can either continue its exploration or depending on the score between this nest and its mother nest, decide to build a bridge between them and to follow it home.


This phenomenon is called swarm intelligence (Bonabeau and Théraulaz, 2000) and is increasingly popular in computer science where centralised control systems are often successfully replaced by other types of control based on interactions between simple elements.

most supervised systems are still ahead.


First, learnability relates directly to parsing technology, as parsers trained on more learnable schemes are likely to obtain higher results.

α s is the empirically preferred annotation of s. Varying syntactic structures are prevalent in many syntactic formalisms (see Section 7).


All but the JEUXVIDEOS.COM corpus were collected in two phases: A first one dedicated to a light study of their contents (lexical differences, required level of preprocessing, etc.).

to conclude, we presented a new data set devoted on French user generated content.


Note that many contractions and typographic diaeresis are built around a verb which is prefixed by a clitic.

finally, we discuss the results of baseline evaluation experiments on POS tagging, including results when using a dedicated wrapper for dealing with noisy texts, and constituency parsing.


It is important to note that the aim of our work is to provide a sample of user-generated texts that are particularly difficult to parse for any parser based on an edited text treebank.

a lot of progress has been made in parsing MRLS using, for examples, techniques built on richer syntactic models, lexical data sparseness reduction or rich feature set.


The details of the calculation for these metrics may be investigated from Nadeau and Sekine (2007).

the number of false positive identifications of organization names decreased.


The underlying reason to adopt the finer NE tagset in the shared task is to use the NER system in various NLP applications, particularly in machine translation.

thereafter the proposed techniques are applied to Hindi and telugu data sets.


The NG is built from right to left in a given sentence.

start Marker (Intermediate marker)* Must-end marker the start-marker and the must-end markers are obligatory to form a VG while intermediate markers are optional and may recurse (*).


Those include two corpora from the MUC conferences (MUC-6, 1995;MUC-7, 1997) and three from the Automatic Content Evaluation (ACE) Program (NIST, 2004).

actions is O(C n), where C is a constant.


At present, there is a single dedicated feature that always fires on this action, but in future work we can use additional features that consider whether it is a good idea to HALT in the current state or to continue merging clusters.

in the future work section we discuss features that express the relative confidence of linking clusters i and j as compared to the alternatives (i.e., is i the only reasonable antecedent for cluster j?).


Participant LE scores the highest amongst all on the LTI index in this conversation.

since leadership and influence are manifested differently and may be deployed by distinct participants in a discussion, it is important for an automatic system to recognize the distinction and make a determination of who is deploying such roles.


Using this simple weighting scheme of taking an average across all measures of the corresponding behavior, our accuracy is ~51% in predicting the top Leader and ~70% in predicting the top Influencer across our test data set.

we have developed a hierarchy of 15 dialogue acts in order to annotate the functional aspect of an utterance in discourse.


On average, across both English and Chinese data, in 44.8% of dialogues different participants held the roles of the (task) Leader and the Influencer.

when computational modules such as local topic detection fail, such errors are systematic, and would be replicated for each participant in their index scores.


To keep true co-references we need to measure the temporal relatedness of terms.

to fully capture temporal co-references we need, in addition to global relatedness measures, a relatedness measure that captures how related terms are during the time periods where they can be related at all.


it is our hope that making these resources publicly available will help foster better standardization in the growing field of NLi.


This set of binary and frequency features is combined to form the "Koppel Baseline."

a Japanese speaker learning English might make the verb tense error shown in (2).


Using parallelization with twenty threads, our implementation achieves an average of 4.8ms per value retrieval.

in a Gaussian kernel K h , more than 99% of the area beneath the curve is within a 3h-radius from the center.


One of the disadvantages of this approach is the increase of the number of model parameters, blowing up the model size and causing a slower classification speed.

this is especially true for the blacklist approach that relies on strong discriminative features that might not be very frequent in all kinds of documents.


In the Serbian model we have 21 maximum probabilities.

certain punctuation differences may also work quite well for distinguishing between languages.


The existing semi-supervised approaches to SRL can largely be regarded as extensions to supervised techniques, as they use supervised learning as sub-routines in the estimation process.

a standard trick to achieve this behavior within the generative framework is to assume that with some small probability ε the true outcome is substituted with a random pick.


In fact, as we will see in Section 4, labeled data mostly provides evidence for combining clusters (thus increasing collocation), and, consequently, the ability to learn granularity is crucial.

nevertheless, efficient inference is possible with MCMC techniques or approximate MAP search methods.


The annotations of explicit and implicit discourse connectives in the Penn Discourse Treebank make it possible to investigate on a large scale how different types of discourse relations are expressed.

pDTB corpus shows that causal relations are not the only ones that are often expressed without an explicit connective, and hence with view on the UID hypothesis are not the only predictable relation (e.g., Restatement is a more implicit relation).


In order to avoid noise in the data, we extracted only those instances where the IC verb was the main verb of an Arg1 which contained only a single sentence.

the most relevant experiment specific to the tendency towards causal inference is again the one by Murray (1997) in which subjects were asked to continue individual sentences that ended with either a period or a connective of the aforementioned types (additive, causal or adversative).


Once again, this analysis supports our argument that combining these three probabilistic models into one unified approach can capture some of the best of each.

we ran 10-fold cross-validation experiments on each collection, selecting parameters that maximize MAP on nine folds and evaluating on the remaining one.


We used Mean Average Precision (MAP) as the evaluation metric.

the MAP score improves considerably for all of the collections, so we can say that P r phrase is superior to P r token .


Similarly, w T 2 occurs in l aligned pairs in which w S 1 is not present, and n is the number of aligned pairs that involve neither w S 1 nor w T 2 .

by setting some higher thresholds M f and M i , we could move the algorithm towards extracting lexicons of higher accuracy, but lower coverage.


If that is not true, we could use SampLEX only to extract source words for which a translation might be found, but the particular translation for each extracted word could then be obtained by some other method.

namely, if we detect that two words constantly co-occur in aligned items randomly sampled from the large corpus, regardless of the surrounding context, it actually strengthens the confidence that those two words really constitute a translation pair.


Then the set of ordered pairs <Ai, Ri> (0≤i≤n) can be used to train an implicit relat ion classifier for labelling Ri(1≤ i ≤ n).

the "outer loop" can be regarded as a supervised learn ing process.


On the other hand, the absence of exp licit textual cues makes it very difficult to identify the implicit d iscourse relations.

(2006) integrated mult iple knowledge sources to produce syntactic and lexical semantic features, which were then used to automatically identify and classify exp licit and imp licit d iscourse relations.


Johansson and Moschitti (2010) demonstrated that the features derived from grammatical and Figure 3: Different dependencies between w i and w i+1 that can be linked by one or two arcs semantic role structure can be used to improve the detection of opinionated expressions in subjectivity analysis.

the figures are for the best combination of the features, which will be described later.


Every observation is a string, but from a technical point of view observations can be represented by more complex structures as long as their data type is totally ordered, which is the only assumption about the form of observations made on the level of the CCRFs implementation.

this method is also rather flexible and could be used with respect to a language with other tagset structure than the positional one.


As semantic relation composition proceeded, semantic relations tend to move toward # 3 .

both Wang and Manning (2010) and Chang et al.


how many arguments are shared by the two predicates (NUM_SHARED_ARGS)?

(2010) consider only entailment and non-entailment, while our proposed model identifies a rich set of relations: paraphrase, forward entailment, backward entailment, contradiction, and independence.


This breakdown shows that the proposed method is particularly good at Contradiction and Forward-Entailment relations, outperforming all other methods on all data sets.

r P e i can be seen as the sentence-level semantic relation between T and the sentence which can be obtained by applying the edit e i to T .


Most current RNNLMs only use one single feature stream, i.e., surface words.

first, we employ a Kaldi speech recognizer (Povey et al., 2011) to decode each utterance using the trained AM and the 3-gram LM.


(3) Although the size of the parts-of-speech is the   Table 4), they have the largest impact on our factored RNNLM.

this paper uses RNNLM as a baseline and improves it by incorporating additional information other than surface words, such as morphological or syntactic features.


From Table 2 we can see that again the four multilingual methods outperform the single language methods in terms of macro precision, recall and F1-measure as well as macro accuracy.

if the sentence contains at least one private state, whose opinion strength is higher or medium, we regard it as subjective.


(2008) then proposed to generate resources and tools for new languages (Spanish and Romanian) using machine translation and cross-lingual annotation projections.

moreover, an exponential loss function is guaranteed to be minimized over the multilingual data.


Figure 2: Per-token accuracies for out-of-domain POS tagging.

the task is to assign words with one of the POS tags from the Penn treebank POS tags (Marcus et al., 1993) and two more tags from MEDLINE dataset.


We used the standard training set, consisting of sections 02-21 of the Wall Street Journal (WSJ) portion of the Penn Treebank, and conducted tests on the Brown corpus (Kucera and Francis, 1967).

comparing to unsupervised representation learning, the semi-supervised representation learning does not require additional annotation effort, but makes use of the existing labels in the source domain.


However, as grammar is used to generate target translations, it's reasonable to decide the quality of a grammar by testing what best translations it can produce.

we allow such edges to be added to the hypergraph, in order to make sure that most sentences are reachable.


6.2), our induced grammar is more reusable, and is able to generate better (+8.3 BLEU points) oracle translations than the grammar of the baseline.

such a pipeline artificially brings an undesirable disconnection between translation model and word alignment model (Blunsom et al., 2009;DeNero and Klein, 2010).


They apply this algorithm for generative model-based ITG grammar induction.

the point in a cube corresponds to a hyperedge (see Fig.


Because rule features are used in our model, the multiplication operator ⊗ is only approximately monotonic under this scoring function.

we expect such approximation to be suitable for SMT.


These results are roughly in line with the automatic metrics presented in Figure 1.

to generate paraphrases, we applied a typical phrase-based statistical Mt pipeline, performing word alignment on the data described in table 1 using GIZA++ (Och and Ney, 2003), then extracting phrase pairs and performing decoding using Moses (Koehn et al., 2007).


Among these are "translations" of the plays into colloquial English, as well as dictionaries that provide modern equivalents for archaic words and phrases.

it also appears that the correlation between BLEU and human style judgments is too low to be of practical use for evaluating style.


We separately show, for these two models, a comparison of the features' effects on them in Figure 2.

eSL students' mistakes in a corpus that fall into the proposed confusion set list as the coverage of the confusion set list on that corpus.


As mentioned before, most of the event recognition and classification methods include three phases: pre-processing, event mention detection and event attributes extraction.

event recognition in this system includes two types of processing: lexical, using specific dictionaries and contextual, using a number of rules.


Most previous methods use statistics such as the TFIDF based cosine measure of word vectors, whose elements are words appearing in the summaries.

for example, the suffix disease is considered to be the hypernym of lifestyle-related diseases.


Whereas, they are semantically related very much in the real context.

sentences with no same words but similar ones could also be considered having a certain degree of semantic similarity.


Moreover, we use a normalized generation probability to indicate the initial bias of sentences towards the query q, i.e., [o] We mark cosine measure as cosine − based, mark Equation 11 based generation probability method as T F − based, and label our proposed generation probability approach based on Equation 12 as wei ght − based.

s 1 is supposed to be more similar with s 2 because it has content s 2 more concerns.


We evaluate the conventional and proposed methods with ASR accuracy (word error rate (WER)), predicate-argument structure error rate (PAER), and test set perplexity in Section 5.

it is difficult to select sentences that semantically match the backend documents because this method is based on surface information.


Also, using all features also results in the highest accuracy (64.81%) in HSK-HSK datasets.

although the Chinese Web POS 5-gram corpus is smaller than the Google Chinese Web 5-gram corpus, the experiments show that they have similar performances on the WOE detection.


If we build an experimental dataset in this ratio, i.e., 8,525 vs. 283,000, the number of correct sentences will dominate the system's performance.

these POS n-grams are treated like words and considered as the features for classification.


Finally, we end with the conclusion and perspectives.

for example, in the matching result M3 of fIGURE 4(b), [Pred] and [A1] are separated by a gap word "减税".


After sentence segmentation, we first build a forest for each sub-sentence pair.

the "*" and "#" denote that the result are significantly better than the adjacent above system and all the other systems respectively (p<0.01).


Comparing these two structures, we can see that our unsupervised tree structure carries more frontier nodes and thus can be factored into more small sub-structures.

fIGURE 4 -The main process of building forest with the CKY-style algorithm.


Furthermore, using our induced unsupervised trees, it accounts for 47.4 frontier nodes on average while there are only 33.9 frontier nodes at most in the traditional linguistic parse trees.

traditional tree-based systems can not work at all for language pairs without any syntactic resource, which has greatly restricted their application.


We further implement head binarization on the English parse trees and apply the achieved binary trees to another string-to-tree system (abbreviated as s2t-hb) with the same settings of s2t.

just as FIGURE 4(c) shows, there are only 3 frontier nodes in the former case ( [0,1] [ Practically, we need a supervised POS tagger, which impairs the unsupervised property of our tree structure to s y unsup utomatic ome extent.


Potential future works may include using semi-supervised methods to incorporate unlabeled data, or use more powerful methods to extract the characters of abbreviations in web data.

a CRFs model is a type of discriminative probabilistic model most often used for the labeling or parsing of sequential data.


Correspondingly, assuming the output of JTrans is (t , the score function of JTrans(JGraph) becomes: The Guided Graph-based Joint Model: JGraph(JTrans) The Guided Transition-based Joint Model: JTrans(JGraph)  We can see that the feature functions are modified to include additional argumentst JGraph and d JGraph .

these models resolve the joint task by a two-step process: (1) constituent parsing and (2) rule-based transformation from constituent structures (CS) to dependency structures (DS).


"Cambridge, Massachusetts" contains "Cambridge").


This is because "Communist Party of China" is likely related with the topic "politics", but "the city of New York" does not have such particular topic.

a practical issue that arises in estimating u only on a q is the paucity of labeled instances for some queries.


feature "acronym" is true), certain surface features effective for linking to "Communist Party of China" may be also effective for disambiguating "NY", and vice versa However, with the gap in other aspects between the distributions of A q and M shown in Section 1, directly adding M to our training set will produce a lot of noise with respect to the queried name.

it shows that we have successfully created non-empty A q for 96% of the 1,891 queries.


Because the knowledge base usually contains millions of entries, it is time-consuming to apply the disambiguation algorithm to the entire knowledge base.

this also supports our assumption about the existence of the document with unambiguous synonyms in the document collection.


The relevant prediction problems may only has shared structure with target problem over certain features.

the total number of available manually labeled instances M for other irrelevant names is relatively large.


7) and F={CF+SeF+SuF+GS} indicates that use of the feature split indeed improves disambiguation performance.

same with other previous work, they only trained their model on data set M without considering the knowledge related to the queried name.


as shown in Figure 1, the previous approaches disambiguate a mention of the name (e.g.


1 is extended to: 6 Experiments and Discussions In our study, we use TAC-10 8 KB and document collection to evaluate our approach for entity linking.

firstly we investigate the amount of the labeled instances created for each query.


This is due to the fact that our term weighting scheme captures the relations between term pairs and get better term weighting allocation.

the dependency relevance can be defined as: where b is a constant larger than 1, which is selected based on a development set comprising 28 questions, which are randomly sampled from our querying collection.


As cQA services contain large scale question and answer (Q&A) archives, they offer an invaluable information resource on the Web, to provide answers to new questions posed by the users (Jeon et al., 2005b).

as discussed before, smaller α in algorithm 1 means initial term weighting scores dominate the term reassignment task, and ignore the dependency relations at all when α trends to zero.


Following (Goldwater et al., 2011), we will learn morphological segmentations for verbs in the WSJ corpus with the input of raw text only.

in most previous work on unsupervised learning of morphology, only the type-based evaluation is reported.


especially, the proposed user behavior features evidently improve the classification performance.


On the one hand, related queries can be used for query suggestion and recommendation, using which the users can extend their search interest and get some related information.

in the first step, we induce candidate pattern pairs from query pairs co-occurring within the same query sessions, while in the second step, we recognize paraphrase patterns from the candidates using a classifier.


To further analyze why traditional monolingual approaches fail to give the satisfactory results for solving the word ambiguity and lexical gap problems, we identify two key challenges in adapting traditional monolingual translation approaches for question retrieval (Jeon et al., 2005;Xue et al., 2008;Zhou et al., 2011).

table 3 shows the distribution across first-level categories of the questions in the archives.


Generally, p(w|θ q ) is calculated with maximum likelihood estimation (MLE): To calculate p(w|θ d ), several smoothing methods have been proposed to overcome the data sparseness problem of a language model constructed from one historical question (Zhai and Lafferty, 2001).

2012AA011102), the National Basic Research Program of China (No.


河 河 河岸 岸 岸(hé é éà à àn) ？ 词汇鸿沟 company 公司(gōngsī) firm 公司(gōngsī) rheum 感冒(gǎnmào) catarrh 感冒(gǎnmào)  Over the past few years, large-scale question and answer archives have become an important information resource on the Web.

the major difference is that our goal is to improve monolingual question retrieval and not CLIR.


For performance evaluation in all the following experiments, we used EVALB to provide bracket scoring as well as complete match scoring.

the final state is 〈S, φ〉 where S contains a single parse tree with a pre-designated root label and Q is empty.


For Chinese experiments, we used Chinese Treebank (CTB) version 5.1 (Xue et al., 2005) as labeled data.

one direction is to design better training and decoding algorithms.


One possible reason is that the size of Chinese unlabeled data used in the paper is much bigger.

our parser lags behind Petrov and Klein (2007) and the reranking parser (Charniak and Johnson, 2005).


The CR system we use definitely does not aim to compete with current state-of-the-art systems.

it is unable to exploit features that describe already formed clusters of mentions belonging to the same entity.


This result gives empirical evidence that these two sets of features convey complementary information that is relevant for the task at hand.

over 64 days, 796 mobile phone conversations from 62 volunteering subjects (20 female) were recorded.


As pointed out in Cohen and Ruths (2013) this causes some issues on transfer to a novel dataset, because the selected features were not representative of differences between liberals and conservatives in the second dataset.

this approach has been shown to be generally superior to LDA on the domain of topic modeling (Chen et al., 2011), but has not been tested for cultural modeling.


Serialization methods also perform better than the tf-idf baseline for induced topics, as shown in Figure  3 and Table 9.

the content users intend to express is longer than a tweet but shorter than a typical blog post.


We primarily focused on Korean Twitter data, but we believe that the methods developed here are also applicable to other languages with similar phenomena.

if data contains some users while leaving out their friends, replies to these friends are not serialized by Reply criteria.


in (Socher et al., 2012), it is proposed a matrix-vector recursive neural network model for semantic compositionality, which has the ability to learn compositional vector representations for phrases and sentences of arbitrary length.


If k only appears in the gf lexicon, then p k,gf = 1 − and p k,bf = .

previous work in NLp on sentiment analysis has mainly focused on explicit sentiments.


Different from their work, which do not cover all cases relevant to gfbf events,  defines a generalized set of implicature rules and proposes a graph-based model to achieve sentiment propagation between the agents and themes of gfbf events.

we define p k,r so it is inversely proportional to the length of the path.


Further, to make sense of a gfbf triple agent, gfbf, theme , where, e.g., the local detectors label it pos, bf, pos , the framework is choosing the smaller one from (a) −1 * p k,r * u k,r (it has a reverser) versus (b) 1 * ξ ikj (it is an exception to the rules).

their work addresses discourse relations among explicit opinions in different sentences.


Therefore, we tune the parameters α p and β l by alternatively adjusting the common factor a via grid search.

the second baseline is CNMF, which is trained on each category without considering the shared topics.


To solve the lexical gap problem, most researchers focused on translation-based approaches since the relationships between words (or phrases) can be explicitly modeled through word-to-word (or phrases) translation probabilities (Jeon et al., 2005;Riezler et al., 2007;Xue et al., 2008;Lee et al., 2008;Bernhard and Gurevych, 2009;Singh, 2012).

while the term-topic matrix U p can be represented as Np ] ∈ R (Ks+Kp)×Np be the topic-question matrix corresponding to category c p , in which each column denotes the question representation in the topic space.


Solutions 3, 4 and 5 are nondominating to each other.

results are reported for F b 3 only as all other metrics behave correspondingly and are reported in Table 4.


In order to draw conclusive remarks, we present an exhaustive evaluation where our MOO algorithm (MOO-clus) is compared to the most competitive text-based (endogenous) SRC algorithms: STC (Zamir and Etzioni, 1998), LINGO (Osinski and Weiss, 2005), OPTIMSRC (Carpineto and Romano, 2010) and GK-means (Moreno et al., 2013).

the results are illustrated in table 3 where we provide values for all the metrics for open source implementations and reported values in the literature for the other experiments i.e.


As such, the subtopics cover most of the query-related senses.

determining a unique and complete metric to evaluate the performance of a clustering algorithm is still an open problem (Amigó et al., 2013).


Every day millions of people search for images on the web, both professionally and for personal amusement.

the Visual Dependency Representation is a directed labelled graph over the regions of an image that captures the spatial relationships between regions.


For such images, objects that are unrelated to the target action form an effective context, which allows this image to be placed at the top of the ranking.

the approach we adopted in this paper is to compare Visual Dependency Representations of images by decomposing the structure into a set of labelled and a unlabelled parent-child subtrees in a depth-first traversal of the VDR.


PAC-Bayesian bounds, however, place a prior over the hypothesis space while making no assumptions on the data generating distribution (McAllester, 1998).

given a d-dimensional product space X (1) × ... × X (d) and a collection of N samples, S, independent and identically distributed (i.i.d.)


In addition, we propose to use morphological information as auxiliary supervision.

although they made use of the co-occurrence of morphemes and words, the context information is lost after chopping the words and feeding the neural network with morphemes.


Representative works in this field include feed-forward neural network language model (NNLM) (Bengio et al., 2003), recurrent neural network language model (RNNLM) (Mikolov et al., 2010), and the recently proposed continues bag-of-words (CBOW) model and continues skip-gram (Skip-gram) model (Mikolov et al., 2013a).

after that, the combined embedding vector is mapped back to the 1-of-v space by another weight matrix M , and the resulting vector is used to predict the central word after conducting softmax on it.


To make a fair comparison with the csmRNN model, we conducted the same settings in our experiments as csmRNN.

we removed all non-Roman characters and mapped all digits to English words.


What makes SCWS different from WS353 is that the words in SCWS are contained in sentences, i.e., there are 2003 pairs of sentences containing these words, while words in WS353 are not associated with sentences.

n w = 10), the model tends to learn more fine-grained separations of the word's different meanings.


Word w's embedding vectors are denoted as V w ∈ R d×N w .

then the conditional probability is described as where ς (x) = 1/(1 + exp(−x)) is the sigmoid function, and U w O ,t specifies the d-dimensional parameter vector associated with the t-th node in the path from the root to the leaf node w O .


• Prepositions: in this case we consider bilexical operators associated with a preposition, which model the probability of a head noun or verb above the preposition given the noun below the preposition.

we will test the performance of the model on heads that do not appear in D. We assume that we are given access to a distributional representation function φ : V → R n , where φ(x) is the n-dimensional representation of x.


The statistics of TS-Lex and other sentiment lexicons are illustrated in Table 1(a).

our method contains two part: (1) a representation learning algorithm to effectively learn the continuous representation of phrases, which are used as features for phrase-level sentiment classification, (2) a seed expansion algorithm that enlarge a small list of sentiment seeds to collect training data for building the phrase-level classifier.


Let E i j be the set of entities of the tweet T i j .

moreover, the political spectrum would be more diverse and should increase the catalog of political parties.


We also observed that the upper bound performance for the reranker still leaves large room for improvement.

spoken Language Understanding aims to interpret user utterances and to convert them to logical forms, or, equivalently, database queries, which can then be used to satisfy the user's information needs.


Hashtags have been proven to be useful for many applications, including microblog retrieval (Efron, 2010), query expansion (A. Bandyopadhyay et al., 2011), sentiment analysis (Davidov et al., 2010;Wang et al., 2011).

hence, microblogs have also been widely used as data sources for public opinion analyses (Bermingham and Smeaton, 2010;, prediction (Asur and huberman, 2010;Bollen et al., 2011), reputation management (Pang and Lee, 2008;Otsuka et al., 2012), and many other applications (Sakaki et al., 2010;Becker et al., 2010;Guy et al., 2010;Guy et al., 2013).


There are also possible extensions to the lexical/morphological features -either in the direction of semi-supervised learning and adding for example features based on latent semantics, topic models, or graphical models popular in the sentiment analysis field , or the direction of deeper linguistic processing in terms of, e.g., syntax/dependecy parsing (but this has limitation given the nature of Twitter data as well as unavailability of such tools for Czech).

we approached the problem mainly from the data-driven perspective (annotation, feature engineering, error analysis).


The grammar and the treebank were developed simultaneously.

the valencies have the same role here as in the formalism of the categorial dependency grammars (detailed in section 3).


Using a mixed dependency representation including both projective and non-projective dependency annotations in one representation, we aim at predicting the projective dependencies in a first step.

for a given sentence, the initial configuration of this system is ( where A is the projective dependency structure predicted by the arc-eager method.


• Our-Heter(N): Our model for heterogeneous networks, proposed in Section 3.2, uses our modified high quality user-tag network as described in Section 4.5.2 and original user-user social network as SMF-2 in Section 4.4.1.

for each item, the tag information is used as bag-of-word representation.


For example, in scientific community, various types of links are formed for different types of objects, i.e., author writes paper, venue publishes paper, reader labels tag, and so on.

in this paper, we expect the proposed model 'Our-Homo' in Section 3.2 and 'Our-Heter' in Section 3.3 can jointly provide a general and systematic solution to handling the following cases of using multiple graphs for recommendation: • Case 1:(Heterogeneous networks with noise) Network data or the extraction process is usually imprecise or noisy in practice.


For instance, in Figure 1, the last punctuation (") is labeled with the cross-clause tag PUS, and its head dominates an IC modifier.

propose a DS-to-PS algorithm, which assumes that a given DS is identical to a flattened version of the desired PS, and then introduce a set of conversion rules.


We gratefully acknowledge the invaluable assistance of Jun Xie in helping us make experiments on dependency-to-string machine translation.

most tried to combine the outputs of constituent and dependency parsers by stacking or bagging.


Figure 3(a) shows the correct PS while Figure 3(b) shows an incorrect parser output.

this analysis demonstrates that coordination and punctuation should be tackled carefully for the conversion between PS and DS, which we do in the design of our treebank.


Both Zhu (1982) and Chen (1999) state that if two phrases have constituents with the same phrasal categories and the dependency types between them are also the same, the phrasal categories of their combinations must be the same.

bhatt and Xia (2012) further described 7 phenomena of incompatibility in the conversion from DS to PS, mainly involving the annotation of empty categories, yet coordination structure and punctuation were not discussed.


Three reordering strategies are further proposed to generate candidates with correct word ordering and reduce the numerous number of candidates generated by permutation.

words in the segments containing wOEs are reordered to generate candidates that may have correct word orderings.


Our method can be characterized as training data identification: we exploit given conditions in patents for our search of training data.


Our methods are language-independent to the extent that a body of patents exists for many common languages.

to this stands automatic terminology recognition (ATR), which we define as finding known terms and their variants (Jacquemin and Bourigault, 2003).


This score gives an idea of the distance of the frequency of an n-gram in a web page from the general distribution of this n-gram in the background set.

to identify the social web pages we obtain a list of social media platforms from Wikipedia 4 , so when looking at the URL of a web page, we can detect if it corresponds to any of those social media platforms.


Capitalized n-grams usually are Named Entities (organizations and company names, locations or other person names related with the individual) or information not detected by some NE recognizers as for example, the title of books, films, TV shows, and so on.

the combination of z-score and Jaccard gets the best BR results, but the BP results are the lowest.


Since weighted Jaccard coefficient needs non-negative entries and we want the cosine similarity of two documents to range from 0 to 1, we translate the values of the z-score so that they are always non-negative.

a web page W is initially represented as the sequence of tokens starting in uppercase, in the order as they appear in the web page.


The final dataset consists of 182 games with annotations in English and known player rating.

conversely, white should try create posts for his knights in black's territory.


We Acc r ρ sig  Table 4: Ranking results for standalone master similarity and SVM (linear and RBF kernel).

it is of course not universally true, which leads to the misclassification of some experts as amateurs.


Other scientists (Chatzidimitriou-Dreismann et al., 1996), however, have shown that this distinction is not universal and lacks all biological basis.

from the figure, we see that the head parts of the curves are overlapped, which correspond to the high frequency parts of the combined n-gram lists, while the tail parts of the curves are divergent.


A number of different text features were computed against each document.

we have measured the correlation between the outcomes of two questions corresponding to each of the fifty annotators; and found that in each case the correlation was greater than 0.8 (p < 0.05).


SVM has been used to identify grammatical patterns within a text and classification based on it (Schwarm and Ostendorf, 2005;Heilman et al., 2008;Petersen and Ostendorf, 2009).

while percentage accuracy is an indicator to how well the model has performed to classify, R indicates the extent of explanatory power it posses.


WSI actually is target-word-specific, which means the conceptualization should be done specifically to different target words.

the average running time for each target word is about 7 minutes on a computer equipped with an Intel Core i5 processor working at 3.1GHz and 8GB RAM.


One straightforward way to deal with this problem is to repeatedly try different numbers of senses on a development set and select the best performed number.

our basic model can be naturally extended into a BNP model as shown in Figure 3.


To address these problems, we first propose a parametric Bayesian model which jointly finds conceptual representations of context words and the sense of the target word.

this should be done in principle on a word-by-word basis, and thus could be time-consuming and prohibitive when there are lots of target words to be concerned.


Most of existing lexical-semantic networks have been built by hand (like for instance WordNet (Miller et al., 1990)) and, despite that assisting tools are generally designed for consistency checking, the task remains time consuming and costly.

in this article, we first present the principles behind the lexical network construction with crowdsourcing and games with a purpose (also known as human-based computation games) and illustrated them with the JeuxDeMots (JDM) project.


At the time of writing of this article, the JeuxDeMots consists in more than 6 800 000 relations betweeen 310 000 terms.

the statistics were made on the terms on which the scheme has proposed inferences identify polysemous terms and refinements.


Then, we present the outline of an elicitation engine based on an inference engine using deduction, induction and especially relation transfer schemes.

a large number of obvious relations may be missing in the lexical network but are indeed necessary for a high quality resources usable in various NLP applications, or even crucial notably for textual semantic analysis.


Their study also investigated the performance of humans on such a task and found that the machine learning algorithm was more consistent although it was outperformed by one of the expert human analysts.

examples of technologies which could be applied include semantic role labeling, (Swier and Stevenson, 2004) deep syntactic parsing, (Lucic and Blake, 2011), and LDA for detecting levels of metaphor (Heintz et al., 2013), in order to obtain a clearer picture of the stylistic structure of such documents.


Since the fuzzy match intervals are divided according to the TM database, the translation result of the SMT system at interval [0.8, 0.9) even slightly outperforms that at interval [0.9, 1.0).

(0.0, 1.0) Table 3: In-Domain Translation Results (BLEU).


It would be interesting to compare our approaches with theirs in the future.

"SMT" and "Model-III" denote that we do not merge the TM phrasepairs into the SMT phrase table during decoding.


It thus shows that the TM information is very useful.

as mentioned in Section 3.1, we need to merge those TM matched phrase pairs into the SMT phrase table when the TM database and the SMT training set are different.


This category can avoid the drawbacks of the pipeline approaches, and thus achieves superior results when the TM database and the SMT training set are the same.

last, since the example-based machine translation (EBMT, [Nagao, 1984]) is similar to that of using TM, some approaches (Watanabe and Sumita, 2003;Smith and Clark, 2009;Dandapat et al., 2011;2012;Phillips, 2011) also combined EBMT with SMT.


Several features in this task are considered outliers with respect to the same features in the other tasks.

furthermore, as a third baseline, we train a regressor by pooling together training data of all domains, combining source and target data without any kind of task relationship mechanism (SVR Pooling).


Our work is similar to them except that OSM model is substantially different from the TSM model as it integrates both the translation and reordering mechanisms into a combined model.

instead of using both the models, a single model that could switch between different generalized OSMs during translation and choose the one which gives the best prediction in each situation, can be used.


We also tried using OSM models over different numbers of clusters simultaneously for English-to-Spanish, English-to-French and English-to-Dutch pairs in an effort to explore whether using different numbers of clusters to classify data provides different information.

both translation and reordering decisions can benefit from richer generalized representations.


To verify that the inconsistency between BLEU and human evaluation scores is due to name spelling variations which humans capture but automatic metrics does not, we recomputed BLEU scores after normalizing spellings of the system outputs to be consistent with the reference translation spelling.

incorrect name classing at the pre-processing step hurts the translation quality for the system name_t.


This may happen because no input analogy is identified, or because none yields an output equation with solutions.

last, we investigated here the translation of sequences of characters on modestly sized tasks.


Searching input analogies Identifying input analogies for an input term u is an operation a priori cubic in the size of the input space.

tackling broader tasks, such as those typically considered in MT evaluation campaigns is currently too challenging.


We observe a clear improvement over each system: almost 10 absolute accuracy points on average are gained by this combination (38.6%).

we observe that ranking analogical candidates according to random forests improves the performance of the analogical device, over training a classifier, as proposed for instance in (Langlais, 2013).


Using this score, it is then possible to measure the significance of an individual's accommodation within a specific communication pair: does each individual accommodate more or less than their personal norm?

we use a set of nine such features, taken from the linguistic style matching study conducted by Ireland et al.


There are a number of additional possibilities for future work.

divergent communication does not neccessarily have negative connotations for the relationship between speakers and recipients.


By default, the STS-2012 baseline outputs values between 1 and 5.

the results of using coreferential similarity are evaluated on a dataset of manually segmented chapters from a novel (Kazantseva and Szpakowicz, 2012) and on transcripts of lectures in Artificial Intelligence (Malioutov and Barzilay, 2006).


One version of TextTiling (Hearst, 1997) uses lexical chains manually constructed using Roget's Thesaurus.

2 why it makes sense to distinguish between these two types.


Let x 1 be a tweet with the following words (the numbers after the colon are TF-IDF values): x 1 = {obama:5.5, medicare:8.3, website:3.8} which has two nearest neighbors: x 27 = {obama:5.5, medicare:8.3, website:3.8, down:5.4} x 356 = {obama:5.5, medicare:8.3, website:3.8, problem:7.0} Then there are two additional words added in x 1 whose values are averaged.

we chose a subset of hashtags from the most frequent hashtags to create groundtruth labels: we manually removed some tags from the subset that are not topic-related (e.g., #truth, #lol) or are ambiguous; we also removed all the tags that are referring to TV series (the relevant tweets can be trivially obtained by named entity matching).


Moreover, these new words are treated differently by assigning a new weight w n to them, since we believe that the new words are not as informative as the original words in the tweet.

for each tweet, we incorporate additional words from its neighboring tweets.


The precision of classification of class members decreases slightly with the inclusion of unmarked contexts, although the differences are not statistically significant (p<0.1).

section 3) and still yielded comparable results.


This heterogeneity is made apparent by an analysis of the overall distribution of the marked cues between the members of each lexical semantic class.

(2012)"s context patterns to extract class-indicative, or marked, distributional information regarding the nouns to classify.


For a fair comparison, the baseline classification model was obtained using the context patterns described in Bel et al.

information regarding all 32 unmarked contexts was provided to the classifiers for all lexical classes considered.


second, the algorithm to learn the inference model should be efficient.


For example, the action sequence is A a 1 A b 1 A a 2 A b 2 • • • A a n A b n when t = 1. t can be negative as well, denoting the reverse constraints.

first, the joint models are slightly better when t is above zero, by decoding with the Yamada scheme in advance.


Experimental evidence for the existence of common patterns in the argumentation structure of review texts across domains (Section 5).

even more important for us is the local sentiment flow in a text.


In the experiments, we rely on the provided corpus split with 900 reviews from three hotel locations in the training set, and 600 reviews from two locations in the validation set and test set each.

even the local sentiment flow in a1 rather measures the impact of local sentiment at different positions.


While the utility of document characterization by genre for empirical language analysis is widely acknowledged, there is relatively little agreement on methodology.

the earliest large-scale statistical study of genre is that of Biber (Biber, 1988), who applied principal component analysis (PCA) to a one-million word corpus consisting of heterogeneous varieties of spoken and written discourse in order to identify multiple dimensions of variation in language.


Three methods merge lists of results from topically-separated queries: SimM only considers their similarity with the fragment; Round-robin picks the best document in each list; and our proposal, DivM, considers the diversity and importance of topics.

a new approach will be proposed, and will be compared in particular to a version of the explicit diversification approach (Santos et al., 2010) adapted to our problem.


Our vision is that since graphics have structure and content, the users whose particular information needs could be satisfied by an infographic will formulate their queries to indicate the requisite structure of the desired graphics.

the models using all 56 features produced significantly better results than the baseline model that used just the general features, indicating that structural and content-based features are very important and must be taken into account in graph retrieval.


Section 5 discusses the features used in our experiments, Section 6 discusses the learning algorithms, Section 6.1 compares the resultant models with a baseline that uses just general features treating query and graphic each as one bag of words, and Section 6.2 discusses the features that appear most influential in the models.

the structure and content of the graph are not taken into consideration.


In a way this is not so surprising; we would expect the musical style to change over shorter times spans reflecting for example changes in taste regarding instrumentation and recording technology (live drums vs. drum computers, use of auto-tune effects etc.

19 It can be seen that length contributes most, followed by slang use, type-toke ratio, POS/Chunk  tags and the more semantic features imagery and pronouns.


The n-gram model hones in on the topic of a text, while the extended model captures more abstract structural and stylistic properties.

unlike in the previous experiment, the extended model now outperforms the topK on two genres (Pop/Rock and Metal).


The above discourse relation labeling tasks are done on the datasets of different size for different languages at the intra-/inter-sentential levels, thus the results cannot be compared directly.

comparison is used to show the difference between two arguments.


It may be a word appearing in a word-pair connective, e.g., "雖然" (suī rán, although), or a word existing individually, e.g., "以及" (yǐ jí, and).

the precisions of Temporal, Contingency, and Comparison recognition by M4 are greatly increased.


Our inference method only requires the ranking of the informativeness scores (and not their exact values).

this experiment indicates the robustness of our approach regarding the genre/domain adaptation.


Since unsupervised methods do not require annotated data for learning a model, employing unsupervised methods has become a popular and important area of research in NLP.

in all cases, the EM algorithm leads to the same ranking (as listed in Table 3).


Besides users' accumulated history data on Zhihu, external knowledge from Weibo is available to help construct the query-user feature vector.

due to the lack of engagement, it is difficult to infer the expertise levels of newcomers.


Wallach 2006proposed the Bigram Topic Model (BTM) which integrates bigram statistics with topic-based approaches to document modeling.

for example, due to sentences like "Beach staffs are rude" and "The hotel has a nice sandy beach," a topic model may put "staff" and "sandy" under a topic for staff and/or put "beach" and "rude" together under the topic of beach views.


Almost all these models regard each topic as a distribution over words.

topic models such as LDA (Blei et al., 2003) and pSLA (Hofmann 1999) and their extensions have been popularly used to find topics in text documents.


One obvious way to consider phrases is to use a natural language parser to find all phrases and then treat each phrase as one term, e.g., "battery life," "sandy beach" and "beach staff."

a commonly used evaluation measure in human evaluation is precision@n (or P@n for short), which is the precision at a particular rank position n in a topic.


Topics in most topic models like LDA are unigram distributions over words and assume words to be exchangeable at the word level.

gPU enables the connection of a phrase with its content words naturally.


LDA is a generative probabilistic model for a document collection.

the GPU inference mechanism is very different from that of LDA, which cannot be reflected in the graphical model or the generative process as it only helps to infer more desirable posterior distributions of topic models.


(2009) proposed a bootstrapping framework called Double Propagation which introduced eight heuristic syntactic rules to detect opinion relations.

annotation 1 is used to evaluate opinion words/targets extraction.


It takes two n-element vectors as input and compresses semantics of the two vectors into one n-element vector in hidden layer by, where [x 1 ; x 2 ] is the concatenation of the two input vectors and f is the sigmoid function; W (dep) is a parameter matrix that is chosen according to the dependency relation between x 1 and x 2 (In the case of y 1 , W (dep) = W (xcomp) ), which is initialized by I i , where I i is a n × n unit matrix, I b is a n-element null vector, and is sampled from a uniform distribution U [−0.001, 0.001] (Socher et al., 2013).

we combine embedding vectors of words in a linking relation by a recursive autoencoder (Socher et al., 2011) according to syntactic dependency structure.


(2012) proposed to use adaptive method for this task.

we integrate latent topical information into the translation model to facilitate the translation process.


In the following, τ w i (w j ) denotes the rank of the word w j in the list of neighbors of w i .

this latest version of the system serves as reference for the rest of this article.


In this way, we can identify the names that form the thesaurus entries and thus compare to existing work.

we can also be interested in a more general way to the relative positions of w i and w j in all neighbor lists of all the words.


It must be first noted that some of the IR similarity measures we used are not symmetric, including Okapi-BM25.

wordNet provides lists of 3 neighbors on average for the 10,473 names of the corpus it covers, while Moby provides lists of 50 neighbors on average for 9,216 names.


These DTs preserve structural information as the dot product between the DTs of two trees approximates the classical tree kernels T K as defined by Collins and Duffy (2002), that is, T K(t a , t b ) ≈ DT (t a ), DT (t b ) .

this function guarantees that two different subtrees have nearly orthonormal vectors (see (Zanzotto and Dell'Arciprete, 2012) for more details).


Comparing rows in the SS columns, we can discover that DST K 0 and DST K +1 behave significantly better than DT K and that DST K 0 behave better than the standard TK.

sTKs, however, are not considered part of the CDsMs family.


The approximation of tree kernels is then given by the fact that .

our DSTKs are positively exploitng distributional semantic information along with structural information.


In (Tenenbaum et al., 2011), the question "How does the mind work?"

the algorithms proposed in this paper build on recent research work on distributional semantic models and manifold representational learning.


on it, do in) whose literal, non-MWE usage is too common for the expression to be used reliably for evaluation; otherwise, we were satisfied that the vast majority of multiword tokens were true matches.

we also evaluate a sample of our multiword vocabulary using crowdsourcing, and offer a qualitative analysis.


DOCREP has undergone an iterative design process during this time as limitations and issues arose, allowing modelling issues to be ironed out and a set of best practices to be established.

while these abstract formalisations are constructive from a theoretical perspective, they do not take into account the runtime performance of abstract representations, nor their ease of use for programmers.


To check if the rule has actually be applied, the input automaton was intersected with the result.

the custom FSA applier function that implements only the necessary features was employed for both rule testing and finding the delimiter cohort.


We can see that although the peak UAS does not increase much, the training curve is more smooth and stable than that without supplement.

in this way, the syntactic structure of a sentence becomes a parse forest (ambiguous labels) instead of a single parse tree.


They provide information about unknown words with some confidence but they do contain some errors.

table 4: F-measures among Methods/OCI dictionary/Dependency LMs on Dialogue test Set the bold values denote the highest F-measures among all methods.


Note that many antecedents were the first or second person.

• exo1/exo2/exog: These are exophoric and denote zero-pronouns of the first person, second person, and the others (general), respectively.


We suppose that this is because the coverage of the Blog and News dictionaries were almost the same, and obligatory cases of predicates are general information regardless of the domain.

we sum up the frequency of the predicate and cases.


Traditional LSA and RNN (Collobert et al., 2011;Bengio, 2009) models turn out to be very slow for feature embeddings.

the training speed of neural language models is too slow for the high dimensionality of features.


These works used manual techniques for identifying the type of support in user messages and hence, are limited to a small number of messages as compared to the real world data.

for the remaining 11% sentences, majority vote was taken with the help of the third annotator.


We present the details of the features used for classification in Section 3.2.

we label each sentence as belonging to either informational or emotional support class and identify the two types of support at sentence level in messages (using binary classification).


(2011) used LBP for parameter estimation.

if shared parameter weights are used, the best learning rate of a weight should be inversely proportional to the square root of the number of connection sharing that weight (Bottou, 2004).


It captures not only the user-behavior dependencies, but also the influence from social ties, for exploring social actions.

the performance of this model is still limited and there is a large room for improving.


The exponential-family random graph models (ERGMs) ( (Frank and Strauss, 1986), (Wasserman and Pattison, 1996)) take the form of an exponential family as is a p-vector of sufficient statistics, which captures network features of interest, its postulated dependence structure, or both.

social actions also have important influence on social ties.


Aspects that directly relate to the events are shown in bold blue, while aspects that have no bearing to the news are shown in italics.

contrast this with Figure 5b for 'organic food'.


One approach is to directly train a linear classifier such as logistic regression using a training set of <phrase,binary label> pairs.

for microblogs, frequency of a noun phrase alone is an insufficient indicator of an aspect, due to the inherent noise (unlike reviews, microblog posts are short and often not as focused) and redundancy (e.g., due to retweeting in the context of Twitter).


(3) As a block-coordinate descent method, we can iteratively alternate between the two optimisation blocks and every limit point of Algorithm 1 is a globally optimal point (cmp.

later in the first half of the 20th century, different textual statistics, such as Zipf's distribution (Zipf, 1932) and Yule's k-statistic (Yule, 1944) have been proposed to quantify textual style.


By using our MKL, selecting features and or kernel functions is no longer necessary as the learning algorithm itself picks the right combination of kernels for the problem at hand.

later, these papers were credited to Alexander Hamilton, John Jay, and James Madison; 73 of the documents are uniquely associated with one of the three authors while the remaining 12, also known as the disputed papers, have been claimed by both, Hamilton and Madison.


It is reasonable to assume that a text passage expressing a broad idea or opinion of an author, often philosophical in nature, is likely to be appealing to readers.

this paper investigated the problem of automated text aesthetics prediction.


Mapping with OSVM results in an improvement in the accuracy at the cost of sacrificing recall, which is not desirable for this problem.

the most effective MC approach uses Gaussian/linear kernels for mapping/convergence (see the seventh row of table 3).


To analyze the result further, we find that the initial importance, automatic extraction to source document, and to background knowledge have different effect on the final performance.

we consider this information by evaluating the relevance between b and every sd-node (Section 3.1).


The merit of such retrieval-based approaches is that, owing to the diversity of the web, systems can retrieve at least some responses for user input, which solves the coverage problem.

because we wanted to compare our system with a state-of-the-art rule-based system, we put a great deal of effort in its development.


A particularly popular model for the evaluation of text coherence is the entity-based local coherence model of Barzilay and Lapata (B&L) (2005;2008), which extracts mentions of entities in the text, and models local coherence by the transitions, from one sentence to the next, in the grammatical role of each mention.

as discussed in Section 1, the main objective of our work is to study the impact of deep hierarchical discourse structures in the evaluation of text coherence.


Discourse parsing aims to identify the discourse relations held among various discourse units in the text.

the average length of the essays is 32 sentences, and the average organization score is 3.05, with a standard deviation of 0.59.


In our re-implementation of B&L, we use the same parameter settings as B&L's original model, i.e., the optimal transition length k = 3 and the salience threshold l = 2.

rST-style discourse relations can be categorized into two types: mononuclear and multi-nuclear.


Since the initial publication of this model, a number of extensions have been proposed, the majority of which are focused on enriching the original feature set.

because of its ability to annotate deep discourse structures and its better coverage of discourse relations, RST-style discourse parsing is generally more powerful than PDTB-style parsing, as far as coherence evaluation is concerned.


LEX-2 features capture the interactions between discourse transitions, indicated by explicit discourse cues, and lexical chaining patterns.

arg-dev:DETAIL), as well as for the primary component alone (e.g.


Type/Token Ratios for Entities (type/token).

it is a strong feature as it can reliably bias the system to change its expectations about the discourse quality based on population and task.


The textual interface displays the search results for the given query as a ranked list of phrases, ordered by decreasing absolute and relative occurrence frequencies.

experimental Design As in experiment 1, we asked the university-level english teacher to design two cloze test questionnaires (see Appendix C and D); for each operator with an easy, a medium, and a hard question.


Carrying out research and development on usage search engines is an interdisciplinary effort that requires expertise from information retrieval, information visualization and interface design, as well as domain knowledge from computer linguistics.

to statistically estimate the per-individual effect, we compare the ratio of correct answers among all answers when the search engine was used to the ratio when it was not used (note that this includes the questions where the engine was allowed but was not used; i.e., columns "manually" and "but not used" in table 1).


The experiment shows that the highly experienced pupils indeed did not use our engine often.

besides this, our user study also showed that the current state of Netspeak's textual user interface as well as the simplified wildcard query language is easy enough to be understood in less than a minute by any newcomer, which demonstrates the low barrier to entry that search engines for common usage have right now.


From what can be said by analyzing their features, all of these services are based on a collection of basic style and grammar rules that can be checked automatically with some degree of confidence in their recommendations.


From their feedback and from our own experience, we know that Netspeak helps to resolve uncertainties on formulations in the daily process of writing papers, proposals, etc.

the predicted benefit for them cannot be confirmed from our small sample.


The majority of the world's population now owns a mobile device and an ever increasing fraction of users are resorting to Short Message Service (SMS) as the primary form of communication.

the results are shown in table 3 and some examples of learned normalizations are shown in table 2.


All these approaches rely on supervised training data to train the normalization model.

an example of the FST translation paradigm is shown in Figure 1 We experimented with the notion of using a consensus-based word alignment by combining the alignment obtained through different alignment tools.


We use the notion of distributional similarity that is automatically induced through the word representations for learning automatic normalization lexicons.

this is beyond the scope of the work presented in this paper.


Supervised, distant supervision, and distant supervision with sampling approaches to temporal evidence classification.

the difference in performance is not large.


Bird, Gelbart, and McAlister spent five days in the village of Cajueiro (Akazu'yw), the gateway to several other Tembé villages that can only be accessed by river.

we generally took care of these tasks on behalf of speakers.


We repeated the above experiments with lists of TCC, LCC and RCC instead of the co-occurrence entropies.

languages represented in scarce quantities on the Web.


Therefore, the words following an adposition (or a function word) mark the beginnings of syntactic units such as noun phrases and are typically restricted to certain syntactic categories.

the details of the corpora used for these four languages are summarized in table 1.


The average number of characters in words of these languages were found to be in the relatively higher range of nine to eleven.

formally, for prepositional languages, r(TL) > r(TR), and ρ(TL) > ρ(TR), while for postpositional languages r(TL) < r(TR) and ρ(TL) < ρ(TR).


Similarly, if RCE or RCC of w is high, it means that a large number of words can follow w in the language (additionally, almost with equal probabilities for high RCE).

for a prepositional language, the function words, which are often the adpositions, precede the content word it is linked to.


The noun predlog 'pretext' has three valence slots: P is a pretext for X for doing Q = 'wishing to do Q, which violates norms of ethics, or politeness, X uses situation P to do Q; he thinks that P justifies Q' (Boguslavskaya 2003).

'Russian books were collected by the late husband of the princess…, while foreign books (dir.


We intend to show (a) that the arguments of adverbials need to be found and identified, however non-trivial this task may be, (b) what their different types are and (c) how the argument structure of adverbial derivatives can be represented in the dictionary.

in the introductory paper to the Special issue on Semantic Role Labeling, the SRL task for adverbials is not even mentioned (Marquez et al.


'the women (Topic) in majority were sitting in the hall' 'most of the women were in the hall' (24b) V zale sideli (Topic) v bol'šinstve svoem ženščiny lit.

their being conversives implies that their lexical functional description should indicate the correlation between their argument structures.


In this paper, we concentrate on statistical approaches for modeling the SLU system.

each pair is extracted from an actual error; for example [phoenix] → [t x] is a result of an ASR error in ATIS-3 logs where "phoenix" in "Show me flights from phoenix …" was recognized as "t x" mistakenly.


In Figure 4, learning curves for five methods of SLD, WGU, LIIS, FIR, and random are shown.

since the number of features of CRF (i.e.


Sincere thanks to the anonymous reviewers for their thorough reviewing and valuable suggestions.

let c be concatenations that concatenate the target edges to generate the target sentence e. The probability of e is defined as： where ϕ i (c) are features defined on concatenations and λ i are feature weights.


We build another set of SMT systems in which language models are combined by linear interpolation 5 .

bLEU score curve is almost tangential zero when k is between 5 and 6 which essentially means that selection of k = 5 is a good choice.


Another similarity metric that can be used is LM Perplexity.

this multi-model is built by a weighted linear combination of the out-domain models (11 models).


(2012a) clustered of a large heterogeneous development corpus and tuned a translation system on different clusters.

in the current scenario we do not have resources (training data) to build a source side LM for computing the perplexity.


Similarly with single system analysis, we generated the top 30 potentially erroneous n-grams for pbmt, hiero, and f2s in two directions, and evaluated them manually.

the regularization factor is chosen from the range 10 −6 -10 −2 to give the highest performance on the KFtt test data.


In practice, we usually use beam search instead.

tree-to-tree approaches still significantly under-perform than tree-to-string systems due to the poor rule coverage (Liu et al., 2009) and bi-parsing failures (Liu et al., 2009;Mi and Liu, 2010).


Since these supervised approaches often has a high requirement on the quality and quantity of annotated corpus, which is always not easy to create.

for the above example, we select the "欧莱雅美宝莲兰蔻 (L'Oreal, Maybelline, Lancome)" and its context "是 (is)"as a seed "欧莱雅美宝莲兰蔻是".


Giménez recently provided us with benchmarks, which he obtained with a somewhat faster processor than ours, the Intel Xeon X5660 2.80 GHz.

define three expanded features: whether the token being tagged has the base feature, whether the preceding token has the base feature, and whether the following token has the base feature.


This produces a lower error rate than either Ratnaparkhi or Ratnaparkhi+ pruning, but Ratnaparkhi+ pruning results in faster tagging.

it has long been standard practice to prune the set of possible tags considered for each word, in order to speed up tagging.


• Initialize S to contain the first k words of V .

if we increase the pruning threshold until we match the Ratnaparkhi+ error rate, as shown in the final "Kneser-Ney+" line, our method is faster than Ratnaparkhi+.


The morph properties are estimated only from the unannotated data.

only a part of the parameters are optimized in each step: the parameters that are used in calculating the likelihood of a certain part, unit, of the corpus.


As in CatML, we apply heuristics for removal of non-morphemes from the final segmentation.

in our example, the morph st cannot be a suffix as it starts the word, it is unlikely to be a prefix as it directly precedes a common suffix ation, and it is unlikely to be a stem as it is very short.


It builds on previous work on Morfessor, sharing model components with the popular Morfessor Baseline and Categories-MAP variants.

a large coverage of suffixes can be achieved already with a relatively small annotated data set.


Although most of these previous researches used syntactic information, the sentences they used there were what had been previously parsed.

from the above, we confirmed the effectiveness of our method on word reordering and dependency parsing of a sentence of which the word order is not easy to read.


Algorithm 1 shows our word reordering algorithm.

we consider it is more desirable to concurrently perform the two processings than to sequentially perform them.


Divisive: a top-down hierarchical clustering algorithm and starts with one, all-inclusive cluster and, at each step, splits the largest cluster until the number of clusters increases to the desired number K, K = 12.

then, our algorithm iteratively calculates the score of topics and sentences until convergence 1 .


The score described in previous section leads to a ranked list of salient and relevant events.

our system is superior to DFIDF * as expected.


A temporal analysis is performed on all documents from the AFP corpus (see §3.1) with the Heideltime (Strötgen and Gertz, 2013) parser.

it does not consider the fact that some information can be redundant between events.


Most previous research on authorship attribution (AA) assumes that the training and test data are drawn from same distribution.

there have been a handful of studies that add some cross-topic flavor in the AA task (Mikros and Argiri, 2007;Goldstein-Stewart et al., 2009;Schein et al., 2010;Stamatatos, 2013).


We believe such a game may be a viable mechanism for generating paraphrase data in diverse domains and languages, cheaply.

this methodology provides a viable means of generating paraphrase corpora, with a small amount of hand-crafted corpus in a new domain.


When the subject and predicate of each sentence matched the subject instance and verb of each pattern, the sentence was marked as "retrieved".

the agreement could be subjective, so we adopted a majority vote from three raters for each sentence.


Then, a list of noun synonyms are gathered from nsubj and root tags (for more information about the tags or relation names, see de Marneffe et al.

next, Stanford Dependency Parser 5 generates dependency information about the words in each sentence.


Inference about whether a word in one text has similar meaning to another word in the other text is an essential task in order to understand whether two texts have similar meaning.

our future research needs to incorporate more syntactic and external knowledge, and to learn more verbs using some of the inferred verbs as seeds.


Only particle verbs composed of two words are assigned to the particle group.

the combination of the simplex verb and the particle is generated as a particle verb.


Since the pair and is not parallel but just comparable, the counterpart of an anchor in is not always found in .

technical words (e.g., "ベイジア ン・ネットワーク" -Bayesian network) and uncommon proper names (e.g., "地獄の辞典" -Dictionnaire Infernal) tend to have high keyphraseness values.


We argue that these patterns are more static than the others, and we call them the customary patterns.

the final results are shown in table 1 the proportions of the four patterns in Plurk and Yahoo are also compared.


The best results are however attained when the two methods are combined, that is, when global transfer is used to fill in missing predicates from direct transfer.

contrary to our predicate labelling model, the role labelling model needs syntactic information in the target language.


Once the correspondences between the syntactic labels of the two syntactic annotation frameworks are discovered, the cross-lingual transfer of syntactic-semantic mappings consists in substituting the English syntactic labels with their French counterparts to adapt the model described above.

to previous work on monolingual unsupervised semantic role induction, we add the predicate label as a predictor.


We conducted our experiments on the multilingual GEOQUERY dataset released by Jones et al.

currently, researchers only focused on the semantic parsing task under a single language setting where the input is a sentence from one particular language.


In other words, the model tries to maximize: Since the correct hybrid tree associated with n-m pair is unknown, we marginalize over the hidden variable h. The model parameters will then be estimated using the Expectation-Maximization (EM) algorithm (Dempster et al., 1977).

one crucial fact is that many such news articles written in different languages are actually all discussing the same underlying story and therefore convey similar or identical semantic information.


Semantics: answer(exclude(river(all), traverse2(stateid( T N )))) In the typical setting, the semantic parser learns from a collection of such sentence-semantics pairs a model that can parse novel input sentences into their respective semantic representations.

each semantic unit can be regarded as a function which takes in other semantic representations of specific types as arguments, and returns a new semantic representation of a particular type.


(2002) showed that much better performance was achieved by using tp, not tf.

we hope that the more uneven the distribution of documents where term t i occurs, the larger the weight of t i is.


(3) Constrained NMTF (CNMTF in short): Li et al.


We use cosine similarity for constructing the p-nearest neighbor graph for its simplicity.

the performance of these methods relies on manually labeled training data.


The horizontal axis is the strength of the proposed penalty, C penalty in eq.(13).

the 3rd left is a model with latent variables that has a category transition penalty, which is the proposed model.


For example, we will polish the Sent Comp model by exploring more sentiment-related features and exploring other types of compression models.

we can obtain the polarity feature (polarity(•)) from a polarity lexicon, which can also be obtained from HowNet.


Based on the above, we can conclude that syntactic features are very important for T-P collocation extraction.

we conducted the experiments on a Chinese corpus of four product domains, which came from the Task3 of the Chinese Opinion Analysis Evaluation (COAE) (Zhao et al., 2008 Camera  138  1,249  1,335  Car  161  1,172  1,312  Notebook  56  623  674  Phone  123  1,350  1,479  All  478  4,394  4,800   Table 2: Corpus statistics for the Chinese corpus of four product domains.


However, their work only focused on English sentences, whereas the relations for Chinese sentences are different.

as discussed in the above sections, the current compression models cannot be directly utilized to T-P collocation extraction owing to the specificity of sentiment sentences.


We designed three comparative systems to demonstrate the effectiveness of Sent Comp for T-P collocation extraction.

to English, the suffix (for noun) or prefix (for non noun) characters of a Chinese word often carry that word's core semantic information.


An alternative recursive partitioning whose root has children labeled {1, 3} and {2} leads to the first hybrid rule in Example 3 (apart from nonterminal names).

the node labeled {1, 3} has children labeled {1} and {3}.


The purpose of this section is to unify existing notions of non-projective dependency structures and discontinuous phrase structures, formalized using s-terms.

the running time of LCFRS parsers is generally very high, still polynomial in the sentence length, but with a degree determined by properties of the grammar; difficulties involved in running LCFRS parsers for natural languages are described by (Kallmeyer and Maier, 2013).


Second, both first-and second-order relations appear among the relations outperforming the baseline.

the addition of second-order dependencies does not change the clustering quality of the baseline (0.196) but increases the number of types.


Manually analysing the errors on the same set of sentences as before, there are again some error categories that occur frequently in both cases such as coordination, the attachment of conjunctions, modifiers and determiners.

in CPs with multiple verbal elements (both a finite verb and an infinitive or a participle in the CP) the arguments were sometimes linked to the wrong verb, as in Figure 6.


All in all, we argue that there are some grammatical phenomena -e.g.

this distinction is rather semantic in nature and was sometimes erroneously annotated in the constituency treebank, which was later corrected in the dependency one and thus now resulted in conversion errors, as shown in Figure 11.


The top ranked systems were pipelines that started with a syntactic analysis (as we do) and continued with predicate identification, argument identification, argument labeling, and word sense disambiguation; cf.

this is not to say that the idea of the surface→surface syntax→deep syntax pipeline is new.


Finally, the idea of modeling event composition can help processing event data in general and can be applied to other tasks like finding co-referent events.

hence, sentences with different labels cannot be separated based on an n-gram language model features.


(2010), we assumed template 1 (T 1 ) has a happens-before relation with template 2 (T 2 ) iff one event expressed by T 1 normally happens before another expressed by T 2 , provided that both events occur.

note that the existence of an L2-link does not guarantee that the semantic properties specified by it hold for all the possible template pairs taken from the class pair it connects.


Accordingly, PPTT can also provide a way to derive various semantic knowledge about this world such as the happens-before relation between events involving an entity, e.g., since the creation phase usually occurs before the execution phase, invent X (creation phase) is likely to happenbefore use X (execution phase).

ppTT can derive more varieties of semantic relations between templates than any one of those existing lexicons.


All continuous features are quantized into 10 discrete bins using cumulative binning (Liu et al., 2006).

the goal of the decoder is to find the best hypothesis for a given input sentence w. A hypothesis h is a label sequence, one label for every word in the sentence.


The disfluencies in speech introduce noise in downstream tasks like machine translation and information extraction.

if the original sentence contains "you know" and it is not labeled as O, a new hypothesis will be generated by labeling it as 'O'.


Below, we first describe this model, and then we introduce our semantic topic model which serves to provide semantic features for the discriminative learner.

we collect from these logs each occurrence of a user click from one Wikipedia page to another during a one month period, from all users in all parts of the world.


In this work we analyzed labeled data in which for a given Topic, the relevant articles were manually identified.

the purpose of the funnel is to gradually focus on smaller and smaller CDC-containing text segments, while filtering out irrelevant text.


Through this process, for the 32 examined Topics, a total of 326 Wikipedia articles were labeled, yielding a total of 976 CDCs.

one intriguing direction, highlighted by examining our data, is the possibility of defining different CDC types.


Most importantly, as implied by its name, a CDC is defined with respect to a given context -the input Topic.

notice, that our definition of a CDC and the associated labeling guidelines -that gave rise to our ground-truth data -imply that in free text articles a CDC often do not correspond to an easily identified sub-tree in the sentence parse tree.


Finally, the average Kappa agreement between pairs of labelers in the confirmation stage was 0.39, which is a relatively high agreement considering the complexity of the labeling task and the inherent elusiveness of the involved concepts.

the candidate boundaries induce a division of the containing sentence into three parts: prefix, candidate body, and suffix, where the prefix and/or suffix might be empty.


The creation of the final corpus consists of two independent tasks.

table 4: Confusion probability matrix for argumentative relation annotations in the previous example an annotator could also link (9) directly to (7) or even to (7) and (8).


Villalba and Saint-Dizier (2012) study argumentation annotation in a corpus of French and English product reviews.

for instance, students post their essays for retrieving feedback about their writing skills while preparing themselves for standardized tests.


Therefore, they annotate a pair of arguments as either entailment or not.

this attribute can take the values for or against.


(3) "Whatever the definition is, camping is an experience that should be tried by everyone."

it is possible to derive argumentative relations between components though they are not explicitly included.


Consider the following example, where the Chinese noun 苹果 is aligned to six English strings (aligned elements are underlined): (1) a. eat apples <> 吃 苹果 b. eat an apple <> 吃 苹果 c. eat the apple <> 吃 苹果 d. fond of apples <> 喜欢 苹果 e. talk about apples <> 谈论 苹果 f. provide them with apples <> 给 他们 苹果 The English apple and the Chinese 苹果 match in meaning and are both unambiguous.

most of the existing parallel treebanks (Li et al., 2012;Megyesi et al., 2010) do not have phrase alignments.


Furthermore, approximately 62% of the triggers have zero-nested holders (i.e.

the contribution of this research, therefore, is twofold: first, we create a novel resource for Arabic NLP which is expected to enhance research on modality automatic identification and extraction; second, we present an efficient and easy-to-administer annotation procedure with interactive crowdsourcing potentials for the complex task of modality annotation.


In example 5, the Twitter user quotes Elbaradei stating that he may run for presidency if the people want him to.

in example 10, the trigger بيتھيألھم bythy>lhm (they imagine) scopes over two complement clauses, which annotators are required to identify.


(2) Wh: Questions with a standard form that ask for specific information using wh-words ("What kind of pet do you have?")

each item consists of a short dialogue fragment showing three utterances before and after the question to be annotated.


Identifying a suitable probabilistic model for our frequency-based BCR's promises to be a fruitful future line of research, as it would allow for a better comparison (and eventually integration) of the two approaches.

we can abbreviate acc i := p(a ij = k | g j = k ).


Disambiguating named entities (NE) in running text to their correct interpretations in a specific knowledge base (KB) is an important problem in NLP.

micro-and macro-averaged accuracy scores of 70.60% and 60.91% respectively were obtained with this baseline.


Macro-averaged accuracy is calculated as shown in equation 10.

they are drawn between two entities when there is a relation between them.


All candidate entities for the different NE textual mentions in the document are represented as an undirected graph G = (V, D) where V is the node set of all possible candidate entities for different NE textual mentions in the input document and D is the set of edges between nodes.

the popularity score dominates the final rank score.


Post-relational states introduced by RNP's modified with "former", "then", and "ex-", however, do appear to unambiguously take the DCT as a time of predication.

it is important to note that while some changes barely affect F-measure, they are important because they allow for correct information that would have otherwise been blocked to be aggregated.


This is important because many queries and slot fillers are expressed only as nominal mentions or pronouns in evidence sentences, their canonical form appearing elsewhere in the document.

we extend the boundaries of entity, time, and value mentions (e.g., people's titles) to include an entire phrase where possible.


Since the cardinalities of the two sets of distinct entity pairs and relations expressions are also very high (See Table 1 of the specification of the ENT dataset), the high dimensions of the two spaces would make the computation cost of MVPLS embedding in terms of time and space prohibitively high.

we can easily gather a set of relation expressions relevant to a given prototype entity pair of a relation.


Consider the sentence "The boy is happy and joyful".

the number of positive labels is substantially smaller than the number of negative labels, 14 and thus this measure can be manipulated: a dummy model that always assigns the negative label gets a high accuracy.


(2012), to detect novel senses of a target word using this WSI method, we jointly topic model two corpora: a reference corpus -taken to represent standard usage -and a focus corpus of newer texts potentially containing novel senses.

identifying diachronic changes in word-sense is a challenge that has only been considered rather recently in computational linguistics.


In the context of multi-document summarization, coherence has been studied widely.

a drawback is that they depend heavily on the RST parser that is used.


Entity Grid: Previous studies showed that Entity Grid (Barzilay and Lapata, 2005) is a strong feature for predicting coherence .

there is an inherent trade-off between the quality of content and linguistic quality.


I don't think piracy hurts them at all !!!

topic models such as Latent Dirichlet Allocation (LDA) (Blei et al., 2003) and its variants are able to discover hidden topics or aspects of document collections, and thus afford a natural way to cluster texts according to their topics.


Rank=2.25, Div Rank=1.25): •Rising costs for movies and music.

hence, it would be beneficial to summarize answers automatically and present the summaries to users who ask similar questions in the future.


These best answers are used to train the statistical ranker that predicts relevance.

we use JSD to rank answers in the ascending order.


In addition, in both travel guides and forum threads, some words are more related to the city being discussed than any specific section.

the objective is to test the coverage of points of interest in our generated summaries versus the summaries generated by ILP-BL.


A natural way to make use of all of them for MR classification is to include them in an ensemble.

finally, we employ human knowledge to identify the constraints on the classification of different relation instances, and enforce the resulting constraints in an integer linear programming (ILP) framework.


To strike a better balance between having a rich representation of the context and improving the learner's ability to generalize, we extract a subtree from a parse tree and use it as the value of the structured feature of an instance.

experimental results on the i2b2 corpus show a significant relative error reduction of 19.8% over a stateof-the-art baseline.


Prior Probability Shift This shift describes changes in the marginal distribution of the output (i.e., the relations).

unfortunately, (Sporleder and Lascarides, 2008) show that a system trained on this type of artificially generated data does not generalize well, leading to important performance degradation compared to a system solely trained on natural data.


Most of these latter errors are mostly imputable to wrong classifications of implicit relations.

we experimented with a maximum entropy classifier from the MegaM 5 package, in multiclass classification, with a maximum of 100 iterations.


To date, there have been only a few attempts at building full document-based discourse parsers.

we may produce examples annotated with a wrong relation or that do not involve any discourse relation at all.


Finally, the evaluator answers the following questionnaire for calculating the evaluation measures in Section 5.1.

we concern ourselves with cooperative persuasive dialogue systems (Hiraoka et al., 2013), which try to satisfy both the user and system goals.


We evaluate the system on the basis of average reward per dialogue with the user simulator.

whereĈ t+1 alt represents the estimated CPD at t + 1.Ĉ t alt represents the estimated CPD at t. The other variables are the same as those in Equation (2).


Traditional reinforcement learning requires thousands of dialogues, which are difficult to collect with real users.

table 3 shows an example of positive framing (p=POS) about the performance of Camera A (a=A).


Note that in the previous section, Sat user and P S sys are estimated from the simulated dialogue.

gPF and CPD are used for calculating naturalness and persuasion success, which are part of the reward function.


we leave the multimodal alignment challenge to future work.


On word pairs that are linked by "complex paths", i.e., more than one rule (lachen V -lächerlich A in Figure 1), our main observation in this respect is that rule paths show a clear "weakest link" property.

in this paper, we have addressed the task of recovering this distinction and called it semantic validation.


(2012) include derivational properties in their feature set to learn Catalan adjective classes.

the second phenomenon that we identified as influencing semantic similarity is markedness (Battistella, 1996).


Also, we find false matches of common noun rules with named entities (Empire N -Empirismus N (Empire -empiricism)).

we learn a binary decision: Semantic relatedness (R) vs. non-semantic relatedness (M, N, C, L) within derivationally related pairs.


learner errors in content words often result from a semantic mismatch between the chosen words.


Table 3 shows a sample result of clustering these concept candidates.

in Section 2, we discuss related work in the field of metaphor interpretation and unsupervised concept induction.


In a similar way, , beginning with a seed set of noun/verb linguistic metaphor pairs, performed spectral clustering on a large set of nouns and verbs in order to predict metaphors which participate in the same conceptual metaphor mapping.

next, we predict that poorly imageable terms (i.e., highly abstract terms) are likely to represent metaphorical usages of s and so are unlikely to be integral to a given literal source domain, so these are filtered out as well.


The Viterbi algorithm is usually used for finding the optimal path.

in the table, slashes indicate the position of the estimated word segmentations and the words that were correctly analyzed are written in bold font.